Relevant bibliographies by topics / Mlp84B

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Academic literature on the topic 'Mlp84B'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Mlp84B"

1

Clark, Kathleen A., Heather Lesage-Horton, Cuiping Zhao, Mary C. Beckerle, and Douglas M. Swank. "Deletion of Drosophila muscle LIM protein decreases flight muscle stiffness and power generation." American Journal of Physiology-Cell Physiology 301, no. 2 (August 2011): C373—C382. http://dx.doi.org/10.1152/ajpcell.00206.2010.

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Muscle LIM protein (MLP) can be found at the Z-disk of sarcomeres where it is hypothesized to be involved in sensing muscle stretch. Loss of murine MLP results in dilated cardiomyopathy, and mutations in human MLP lead to cardiac hypertrophy, indicating a critical role for MLP in maintaining normal cardiac function. Loss of MLP in Drosophila ( mlp84B) also leads to muscle dysfunction, providing a model system to examine MLP's mechanism of action. Mlp84B-null flies that survive to adulthood are not able to fly or beat their wings. Transgenic expression of the mlp84B gene in the Mlp84B-null background rescues flight ability and restores wing beating ability. Mechanical analysis of skinned flight muscle fibers showed a 30% decrease in oscillatory power production and a slight increase in the frequency at which maximum power is generated for fibers lacking Mlp84B compared with rescued fibers. Mlp84B-null muscle fibers displayed a 25% decrease in passive, active, and rigor stiffness compared with rescued fibers, but no significant decrease in isometric tension generation was observed. Muscle ultrastructure of Mlp84B-null muscle fibers is grossly normal; however, the null fibers have a slight decrease, 11%, in thick filament number per unit cross-sectional area. Our data indicate that MLP contributes to muscle stiffness and is necessary for maximum work and power generation.
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2

Stronach, B. E., S. E. Siegrist, and M. C. Beckerle. "Two muscle-specific LIM proteins in Drosophila." Journal of Cell Biology 134, no. 5 (September 1, 1996): 1179–95. http://dx.doi.org/10.1083/jcb.134.5.1179.

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The LIM domain defines a zinc-binding motif found in a growing number of eukaryotic proteins that regulate cell growth and differentiation during development. Members of the cysteine-rich protein (CRP) family of LIM proteins have been implicated in muscle differentiation in vertebrates. Here we report the identification and characterization of cDNA clones encoding two members of the CRP family in Drosophila, referred to as muscle LIM proteins (Mlp). Mlp60A encodes a protein with a single LIM domain linked to a glycine-rich region. Mlp84B encodes a protein with five tandem LIM-glycine modules. In the embryo, Mlp gene expression is spatially restricted to somatic, visceral, and pharyngeal muscles. Within the somatic musculature, Mlp84B transcripts are enriched at the terminal ends of muscle fibers, whereas Mlp60A transcripts are found throughout the muscle fibers. The distributions of the Mlp60A and Mlp84B proteins mirror their respective mRNA localizations, with Mlp84B enrichment occurring at sites of muscle attachment. Northern blot analysis revealed that Mlp gene expression is developmentally regulated, showing a biphasic pattern over the course of the Drosophila life cycle. Peaks of expression occur late in embryogenesis and during metamorphosis, when the musculature is differentiating. Drosophila Mlp60A and Mlp84B, like vertebrate members of the CRP family, have the ability to associate with the actin cytoskeleton when expressed in rat fibroblast cells. The temporal expression and spatial distribution of muscle LIM proteins in Drosophila are consistent with a role for Mlps in myogenesis, late in the differentiation pathway.
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3

Stronach, Beth E., Patricia J. Renfranz, Brenda Lilly, and Mary C. Beckerle. "Muscle LIM Proteins Are Associated with Muscle Sarcomeres and Require dMEF2 for Their Expression during DrosophilaMyogenesis." Molecular Biology of the Cell 10, no. 7 (July 1999): 2329–42. http://dx.doi.org/10.1091/mbc.10.7.2329.

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A genetic hierarchy of interactions, involving myogenic regulatory factors of the MyoD and myocyte enhancer-binding 2 (MEF2) families, serves to elaborate and maintain the differentiated muscle phenotype through transcriptional regulation of muscle-specific target genes. Much work suggests that members of the cysteine-rich protein (CRP) family of LIM domain proteins also play a role in muscle differentiation; however, the specific functions of CRPs in this process remain undefined. Previously, we characterized two members of the Drosophila CRP family, the muscle LIM proteins Mlp60A and Mlp84B, which show restricted expression in differentiating muscle lineages. To extend our analysis ofDrosophila Mlps, we characterized the expression of Mlps in mutant backgrounds that disrupt specific aspects of muscle development. We show a genetic requirement for the transcription factor dMEF2 in regulating Mlp expression and an ability of dMEF2 to bind, in vitro, to consensus MEF2 sites derived from those present inMlp genomic sequences. These data suggest that theMlp genes may be direct targets of dMEF2 within the genetic hierarchy controlling muscle differentiation. Mutations that disrupt myoblast fusion fail to affect Mlp expression. In later stages of myogenic differentiation, which are dedicated primarily to assembly of the contractile apparatus, we analyzed the subcellular distribution of Mlp84B in detail. Immunofluorescent studies revealed the localization of Mlp84B to muscle attachment sites and the periphery of Z-bands of striated muscle. Analysis of mutations that affect expression of integrins and α-actinin, key components of these structures, also failed to perturb Mlp84B distribution. In conclusion, we have used molecular epistasis analysis to position Mlp function downstream of events involving mesoderm specification and patterning and concomitant with terminal muscle differentiation. Furthermore, our results are consistent with a structural role for Mlps as components of muscle cytoarchitecture.
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4

Mery, A., O. Taghli-Lamallem, K. A. Clark, M. C. Beckerle, X. Wu, K. Ocorr, and R. Bodmer. "The Drosophila muscle LIM protein, Mlp84B, is essential for cardiac function." Journal of Experimental Biology 211, no. 1 (January 1, 2008): 15–23. http://dx.doi.org/10.1242/jeb.012435.

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5

Clark, K. A., J. M. Bland, and M. C. Beckerle. "The Drosophila muscle LIM protein, Mlp84B, cooperates with D-titin to maintain muscle structural integrity." Journal of Cell Science 120, no. 12 (May 22, 2007): 2066–77. http://dx.doi.org/10.1242/jcs.000695.

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6

Clark, Kathleen A., and Julie L. Kadrmas. "Drosophila melanogastermuscle LIM protein and alpha-actinin function together to stabilize muscle cytoarchitecture: A potential role for Mlp84B in actin-crosslinking." Cytoskeleton 70, no. 6 (April 18, 2013): 304–16. http://dx.doi.org/10.1002/cm.21106.

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7

Nishiyama, So-ichiro, Daisuke Suzuki, Yasuaki Itoh, Kazuho Suzuki, Hirotaka Tajima, Akihiro Hyakutake, Michio Homma, Susan M. Butler-Wu, Andrew Camilli, and Ikuro Kawagishi. "Mlp24 (McpX) of Vibrio cholerae Implicated in Pathogenicity Functions as a Chemoreceptor for Multiple Amino Acids." Infection and Immunity 80, no. 9 (July 2, 2012): 3170–78. http://dx.doi.org/10.1128/iai.00039-12.

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ABSTRACTThe chemotaxis ofVibrio cholerae, the causative agent of cholera, has been implicated in pathogenicity. The bacterium has more than 40 genes for methyl-accepting chemotaxis protein (MCP)-like proteins (MLPs). In this study, we found that glycine and at least 18l-amino acids, including serine, arginine, asparagine, and proline, serve as attractants to the classical biotype strain O395N1. Based on the sequence comparison withVibrio parahaemolyticus, we speculated that at least 17 MLPs ofV. choleraemay mediate chemotactic responses. Among them, Mlp24 (previously named McpX) is required for the production of cholera toxin upon mouse infection.mlp24deletion strains of both classical and El Tor biotypes showed defects in taxis toward several amino acids, which were complemented by the expression of Mlp24. These amino acids enhanced methylation of Mlp24. Serine, arginine, asparagine, and proline were shown to bind directly to the periplasmic fragment of Mlp24. The structural information of its closest homolog, Mlp37, predicts that Mlp24 has two potential ligand-binding pockets per subunit, the membrane distal of which was suggested, by mutational analyses, to be involved in sensing of amino acids. These results suggest that Mlp24 is a chemoreceptor for multiple amino acids, including serine, arginine, and asparagine, which were previously shown to stimulate the expression of several virulence factors, implying that taxis toward a set of amino acids plays critical roles in pathogenicity ofV. cholerae.
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8

PIASECKI, Adam, Jakub JURASZ, and Rajmund SKOWRON. "FORECASTING SURFACE WATER LEVEL FLUCTUATIONS OF LAKE SERWY (NORTHEASTERN POLAND) BY ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION." Journal of Environmental Engineering and Landscape Management 25, no. 4 (December 21, 2017): 379–88. http://dx.doi.org/10.3846/16486897.2017.1303498.

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The aim of this study is to assess the possibility of forecasting water level fluctuations in a relatively small (<100 km2), post-glacial lake located in a temperate climate zone by means of artificial neural networks and multiple linear regression. The area of study was Lake Serwy, located in northeastern Poland. Two artificial neural network (ANN) multilayer perceptron (MLP) and multiple linear regression (MLR) models were built. The following explanatory variables were considered: maximal and minimal temperature (Tmax, Tmin) wind speed (WS), vertical circulation (VC) and water level from previous periods (WL). Additionally, a binary variable describing the period of the year (winter, summer) has been considered in one of the two MLP and MLR models. The forecasting models have been assessed based on selected criteria: mean absolute percentage error (MAPE), root mean squared error (RMSE), coefficient of determination (R2) and mean biased error. Considering their values and absolute deviations from observed values it was concluded that the ANN model using an additional binary variable (MLP_B+) has the best forecasting performance. Absolute deviations from observed values were the determining factor which made this model the most efficient. In the case of the MLP_B+ model, those values were about 10% lower than in other models. The conducted analyses indicated good performance of ANN networks as a forecasting tool for relatively small lakes located in temperate climate zones. It is acknowledged that they enable water level forecasting with greater precision and lower absolute deviations than the use of multiple linear regression models.
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9

Filatov, P. V., A. E. Kalinina, T. R. Kudryashova, A. O. Gorbunova, A. A. Kryukov, O. B. Ivanchenko, E. B. Aronova, and A. P. Yurkov. "The effect of inoculation with Rhizophagus irregularis on the expression level of phosphate transporter genes in the roots of Medicago lupulina under conditions of low phosphorus level in the substrate." IOP Conference Series: Earth and Environmental Science 1096, no. 1 (October 1, 2022): 012004. http://dx.doi.org/10.1088/1755-1315/1096/1/012004.

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Abstract The dynamics of expression of some genes responsible for phosphorus transport (PT1, PT2, PT4, ATP1) at different developmental stages of Medicago lupulina in roots inoculated and uninoculated with AM fungus were analyzed. The levels of MtPT1 and MtPT2 transcripts were decreased under condition of AM symbiosis development. For the first time it is in M. lupulina AM roots that MlATP1 expression has been analyzed. The dynamics of AM-specific MlPT4 and MlATP1 expression depended on developmental stage of the host plant. The key changes in gene expression were observed at the stemming stage of M. lupulina plants.
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10

Modarres, R. "Multi-criteria validation of artificial neural network rainfall-runoff modeling." Hydrology and Earth System Sciences Discussions 5, no. 6 (December 2, 2008): 3449–77. http://dx.doi.org/10.5194/hessd-5-3449-2008.

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Abstract. In this study we propose a comprehensive multi-criteria validation test for rainfall-runoff modeling by artificial neural networks. This study applies 17 global statistics and 3 additional non-parametric tests to evaluate the ANNs. The weakness of global statistics for validation of ANN is demonstrated by rainfall-runoff modeling of the Plasjan Basin in the western region of the Zayandehrud watershed, Iran. Although the global statistics showed that the multi layer perceptron with 4 hidden layers (MLP4) is the best ANN for the basin comparing with other MLP networks and empirical regression model, but the non-parametric tests illustrate that neither the ANNs nor the regression model are able to reproduce the probability distribution of observed runoff in validation phase. However, the MLP4 network is the best network to reproduce the mean and variance of the observed runoff based on non-parametric tests. The performance of ANNs and empirical model was also demonstrated for low-medium and high flows. Although the MLP4 network gives the best performance among ANNs for low-medium and high flows based on different statistics but the empirical model shows better results. However, none of the models is able to simulate the frequency distribution of low-medium and high flows according to non-parametric tests. This study illustrates that the modelers should select appropriate and relevant evaluation measures from the set of existing metrics based on the particular requirements of each individual applications.
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